Dynamic in-hand manipulation remains a challenging task for soft robotic systems that have demonstrated
advantages in safe compliant interactions but struggle with
high-speed dynamic tasks. In this work, we present SWIFT,
a system for learning dynamic tasks using a soft and compliant
robotic hand. Unlike previous works that rely on quasi-static
actions and precise object models, the proposed system learns
to spin a pen through trial-and-error using only real-world
data without requiring explicit prior knowledge of the pen's
physical attributes. With self-labeled trials sampled from the
real world, the system discovers the set of pen grasping and
spinning primitive parameters that enables a soft hand to
spin a pen robustly and reliably. After 130 sampled actions,
SWIFT achieves 100 % success rate across three pens with
different weights and weight distributions, demonstrating the
system's generalizability and robustness to changes in object
properties. The results highlight the potential for soft robotic
end-effectors to perform dynamic tasks including rapid in-hand
manipulation. We also demonstrate that SWIFT generalizes
to spinning tools with different shapes and weights such as a
brush and a screwdriver which we spin with 10/10 and 5/10
success rates respectively
